{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np # 数据处理最重要的模块\n",
    "import pandas as pd # 数据处理最重要的模块\n",
    "import scipy.stats as stats # 统计模块\n",
    "import scipy\n",
    "# import pymysql  # 导入数据库模块\n",
    "\n",
    "from datetime import datetime # 时间模块\n",
    "import statsmodels.formula.api as smf  # OLS regression\n",
    "\n",
    "# import pyreadr # read RDS file\n",
    "\n",
    "from matplotlib import style\n",
    "import matplotlib.pyplot as plt  # 画图模块\n",
    "import matplotlib.dates as mdates\n",
    "\n",
    "from matplotlib.font_manager import FontProperties # 作图中文\n",
    "from pylab import mpl\n",
    "#mpl.rcParams['font.sans-serif'] = ['SimHei']\n",
    "#plt.rcParams['font.family'] = 'Times New Roman'\n",
    "\n",
    "#输出矢量图 渲染矢量图\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'svg'\n",
    "\n",
    "from IPython.core.interactiveshell import InteractiveShell # jupyter运行输出的模块\n",
    "#显示每一个运行结果\n",
    "InteractiveShell.ast_node_interactivity = 'all'\n",
    "\n",
    "#设置行不限制数量\n",
    "#pd.set_option('display.max_rows',None)\n",
    "\n",
    "#设置列不限制数量\n",
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "股票市场回报率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MarketR</th>\n",
       "      <th>MarketR_e</th>\n",
       "      <th>rfmonth</th>\n",
       "      <th>ret</th>\n",
       "      <th>ret_e</th>\n",
       "      <th>marketret3</th>\n",
       "      <th>marketret6</th>\n",
       "      <th>marketret12</th>\n",
       "      <th>Q</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1991-01-31</th>\n",
       "      <td>0.029998</td>\n",
       "      <td>0.036554</td>\n",
       "      <td>0.006930</td>\n",
       "      <td>0.023068</td>\n",
       "      <td>0.029624</td>\n",
       "      <td>-0.084127</td>\n",
       "      <td>-0.305662</td>\n",
       "      <td>0.254049</td>\n",
       "      <td>1991 Q1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-02-28</th>\n",
       "      <td>0.010203</td>\n",
       "      <td>0.021860</td>\n",
       "      <td>0.006930</td>\n",
       "      <td>0.003273</td>\n",
       "      <td>0.014930</td>\n",
       "      <td>-0.183573</td>\n",
       "      <td>-0.384745</td>\n",
       "      <td>0.241492</td>\n",
       "      <td>1991 Q1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-03-31</th>\n",
       "      <td>-0.099663</td>\n",
       "      <td>-0.060425</td>\n",
       "      <td>0.006930</td>\n",
       "      <td>-0.106593</td>\n",
       "      <td>-0.067355</td>\n",
       "      <td>-0.252928</td>\n",
       "      <td>-0.445049</td>\n",
       "      <td>0.288857</td>\n",
       "      <td>1991 Q1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-04-30</th>\n",
       "      <td>-0.079652</td>\n",
       "      <td>-0.031449</td>\n",
       "      <td>0.006651</td>\n",
       "      <td>-0.086303</td>\n",
       "      <td>-0.038100</td>\n",
       "      <td>-0.234776</td>\n",
       "      <td>-0.394937</td>\n",
       "      <td>0.691749</td>\n",
       "      <td>1991 Q2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-05-31</th>\n",
       "      <td>-0.074521</td>\n",
       "      <td>0.005375</td>\n",
       "      <td>0.006092</td>\n",
       "      <td>-0.080613</td>\n",
       "      <td>-0.000717</td>\n",
       "      <td>-0.236294</td>\n",
       "      <td>0.181673</td>\n",
       "      <td>1.542701</td>\n",
       "      <td>1991 Q2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-08-31</th>\n",
       "      <td>-0.031468</td>\n",
       "      <td>-0.033685</td>\n",
       "      <td>0.001118</td>\n",
       "      <td>-0.032586</td>\n",
       "      <td>-0.034803</td>\n",
       "      <td>0.151050</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2024 Q3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-30</th>\n",
       "      <td>0.195992</td>\n",
       "      <td>0.227078</td>\n",
       "      <td>0.001118</td>\n",
       "      <td>0.194874</td>\n",
       "      <td>0.225960</td>\n",
       "      <td>0.200147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2024 Q3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-10-31</th>\n",
       "      <td>-0.003409</td>\n",
       "      <td>0.070220</td>\n",
       "      <td>0.001118</td>\n",
       "      <td>-0.004527</td>\n",
       "      <td>0.069102</td>\n",
       "      <td>0.000228</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2024 Q4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-11-30</th>\n",
       "      <td>0.009724</td>\n",
       "      <td>0.045312</td>\n",
       "      <td>0.001118</td>\n",
       "      <td>0.008606</td>\n",
       "      <td>0.044194</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2024 Q4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-12-31</th>\n",
       "      <td>-0.002679</td>\n",
       "      <td>-0.040799</td>\n",
       "      <td>0.001118</td>\n",
       "      <td>-0.003797</td>\n",
       "      <td>-0.041917</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2024 Q4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>408 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             MarketR  MarketR_e   rfmonth       ret     ret_e  marketret3  \\\n",
       "month                                                                       \n",
       "1991-01-31  0.029998   0.036554  0.006930  0.023068  0.029624   -0.084127   \n",
       "1991-02-28  0.010203   0.021860  0.006930  0.003273  0.014930   -0.183573   \n",
       "1991-03-31 -0.099663  -0.060425  0.006930 -0.106593 -0.067355   -0.252928   \n",
       "1991-04-30 -0.079652  -0.031449  0.006651 -0.086303 -0.038100   -0.234776   \n",
       "1991-05-31 -0.074521   0.005375  0.006092 -0.080613 -0.000717   -0.236294   \n",
       "...              ...        ...       ...       ...       ...         ...   \n",
       "2024-08-31 -0.031468  -0.033685  0.001118 -0.032586 -0.034803    0.151050   \n",
       "2024-09-30  0.195992   0.227078  0.001118  0.194874  0.225960    0.200147   \n",
       "2024-10-31 -0.003409   0.070220  0.001118 -0.004527  0.069102    0.000228   \n",
       "2024-11-30  0.009724   0.045312  0.001118  0.008606  0.044194         NaN   \n",
       "2024-12-31 -0.002679  -0.040799  0.001118 -0.003797 -0.041917         NaN   \n",
       "\n",
       "            marketret6  marketret12        Q  \n",
       "month                                         \n",
       "1991-01-31   -0.305662     0.254049  1991 Q1  \n",
       "1991-02-28   -0.384745     0.241492  1991 Q1  \n",
       "1991-03-31   -0.445049     0.288857  1991 Q1  \n",
       "1991-04-30   -0.394937     0.691749  1991 Q2  \n",
       "1991-05-31    0.181673     1.542701  1991 Q2  \n",
       "...                ...          ...      ...  \n",
       "2024-08-31         NaN          NaN  2024 Q3  \n",
       "2024-09-30         NaN          NaN  2024 Q3  \n",
       "2024-10-31         NaN          NaN  2024 Q4  \n",
       "2024-11-30         NaN          NaN  2024 Q4  \n",
       "2024-12-31         NaN          NaN  2024 Q4  \n",
       "\n",
       "[408 rows x 9 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pandas.tseries.offsets import MonthEnd # 月末\n",
    "Market_ret = pd.read_csv('F:/Python2025/python/datasets/Marketret_mon_stock2024.csv')\n",
    "Market_ret['month'] = pd.to_datetime(Market_ret['month'], format='%b %Y') + MonthEnd(0)\n",
    "Market_ret.set_index('month', inplace=True)\n",
    "Market_ret.sort_index(inplace=True)\n",
    "Market_ret = Market_ret.drop(columns=['Unnamed: 0'])\n",
    "Market_ret         "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cpi</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1987-01-31</th>\n",
       "      <td>5.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987-02-28</th>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987-03-31</th>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987-04-30</th>\n",
       "      <td>6.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987-05-31</th>\n",
       "      <td>7.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-03-31</th>\n",
       "      <td>-0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-04-30</th>\n",
       "      <td>-0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-05-31</th>\n",
       "      <td>-0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-06-30</th>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025-07-31</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>463 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            cpi\n",
       "month          \n",
       "1987-01-31  5.1\n",
       "1987-02-28  5.4\n",
       "1987-03-31  5.8\n",
       "1987-04-30  6.7\n",
       "1987-05-31  7.6\n",
       "...         ...\n",
       "2025-03-31 -0.1\n",
       "2025-04-30 -0.1\n",
       "2025-05-31 -0.1\n",
       "2025-06-30  0.1\n",
       "2025-07-31  0.0\n",
       "\n",
       "[463 rows x 1 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inflation = pd.read_csv('F:/Python2025/python/datasets/inflation.csv')\n",
    "inflation['month'] = pd.to_datetime(inflation['month'],format='%Y/%m/%d')\n",
    "inflation.set_index('month',inplace=True)\n",
    "inflation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "price_dividend = pd.read_csv('F:/Python2025/python/datasets/Price_dividend_mon2024.csv')\n",
    "price_dividend['month'] = pd.date_range(start='1990-12-31', end='2024-12-31', freq='ME')\n",
    "price_dividend.set_index('month', inplace=True)\n",
    "price_dividend.sort_index(inplace=True)\n",
    "price_dividend = price_dividend.drop(columns=['Unnamed: 0'])\n",
    "\n",
    "price_earning = pd.read_csv('F:/Python2025/python/datasets/Price_earnings_mon2024.csv')\n",
    "price_earning['month'] = pd.date_range(start='1991-01-31', end='2024-12-31', freq='ME')\n",
    "price_earning.set_index('month', inplace=True)\n",
    "price_earning.sort_index(inplace=True)\n",
    "\n",
    "price_bookvalue = pd.read_csv('F:/Python2025/python/datasets/Price_bookvalue_mon2024.csv')\n",
    "price_bookvalue['month'] = pd.date_range(start='1990-12-31', end='2024-12-31', freq='ME')\n",
    "price_bookvalue.set_index('month', inplace=True)\n",
    "price_bookvalue.sort_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MarketR</th>\n",
       "      <th>MarketR_e</th>\n",
       "      <th>month</th>\n",
       "      <th>Q</th>\n",
       "      <th>rfday</th>\n",
       "      <th>er</th>\n",
       "      <th>er1</th>\n",
       "      <th>er2</th>\n",
       "      <th>er3</th>\n",
       "      <th>er4</th>\n",
       "      <th>neg_er</th>\n",
       "      <th>neg_er1</th>\n",
       "      <th>neg_er2</th>\n",
       "      <th>neg_er3</th>\n",
       "      <th>pos_er</th>\n",
       "      <th>pos_er1</th>\n",
       "      <th>pos_er2</th>\n",
       "      <th>pos_er3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-12-20</th>\n",
       "      <td>0.050067</td>\n",
       "      <td>0.752547</td>\n",
       "      <td>12 1990</td>\n",
       "      <td>1990 Q4</td>\n",
       "      <td>0.000227</td>\n",
       "      <td>0.049840</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.049840</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-21</th>\n",
       "      <td>0.039671</td>\n",
       "      <td>67.771445</td>\n",
       "      <td>12 1990</td>\n",
       "      <td>1990 Q4</td>\n",
       "      <td>0.000227</td>\n",
       "      <td>0.039444</td>\n",
       "      <td>0.049840</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.039444</td>\n",
       "      <td>0.049840</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-24</th>\n",
       "      <td>0.049380</td>\n",
       "      <td>0.049638</td>\n",
       "      <td>12 1990</td>\n",
       "      <td>1990 Q4</td>\n",
       "      <td>0.000227</td>\n",
       "      <td>0.049153</td>\n",
       "      <td>0.039444</td>\n",
       "      <td>0.049840</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.049153</td>\n",
       "      <td>0.039444</td>\n",
       "      <td>0.049840</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-25</th>\n",
       "      <td>0.049938</td>\n",
       "      <td>0.050022</td>\n",
       "      <td>12 1990</td>\n",
       "      <td>1990 Q4</td>\n",
       "      <td>0.000227</td>\n",
       "      <td>0.049711</td>\n",
       "      <td>0.049153</td>\n",
       "      <td>0.039444</td>\n",
       "      <td>0.049840</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.049711</td>\n",
       "      <td>0.049153</td>\n",
       "      <td>0.039444</td>\n",
       "      <td>0.049840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-26</th>\n",
       "      <td>0.050151</td>\n",
       "      <td>0.050151</td>\n",
       "      <td>12 1990</td>\n",
       "      <td>1990 Q4</td>\n",
       "      <td>0.000227</td>\n",
       "      <td>0.049924</td>\n",
       "      <td>0.049711</td>\n",
       "      <td>0.049153</td>\n",
       "      <td>0.039444</td>\n",
       "      <td>0.049840</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.049924</td>\n",
       "      <td>0.049711</td>\n",
       "      <td>0.049153</td>\n",
       "      <td>0.039444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-12-25</th>\n",
       "      <td>-0.004361</td>\n",
       "      <td>-0.019226</td>\n",
       "      <td>12 2024</td>\n",
       "      <td>2024 Q4</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>-0.004398</td>\n",
       "      <td>0.012003</td>\n",
       "      <td>-0.009967</td>\n",
       "      <td>-0.000314</td>\n",
       "      <td>-0.001943</td>\n",
       "      <td>-0.004398</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.009967</td>\n",
       "      <td>-0.000314</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.012003</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-12-26</th>\n",
       "      <td>0.002969</td>\n",
       "      <td>0.011729</td>\n",
       "      <td>12 2024</td>\n",
       "      <td>2024 Q4</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>0.002932</td>\n",
       "      <td>-0.004398</td>\n",
       "      <td>0.012003</td>\n",
       "      <td>-0.009967</td>\n",
       "      <td>-0.000314</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.004398</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.009967</td>\n",
       "      <td>0.002932</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.012003</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-12-27</th>\n",
       "      <td>0.002119</td>\n",
       "      <td>0.008489</td>\n",
       "      <td>12 2024</td>\n",
       "      <td>2024 Q4</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>0.002082</td>\n",
       "      <td>0.002932</td>\n",
       "      <td>-0.004398</td>\n",
       "      <td>0.012003</td>\n",
       "      <td>-0.009967</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.004398</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002082</td>\n",
       "      <td>0.002932</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.012003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-12-30</th>\n",
       "      <td>0.000660</td>\n",
       "      <td>-0.008647</td>\n",
       "      <td>12 2024</td>\n",
       "      <td>2024 Q4</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>0.000623</td>\n",
       "      <td>0.002082</td>\n",
       "      <td>0.002932</td>\n",
       "      <td>-0.004398</td>\n",
       "      <td>0.012003</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.004398</td>\n",
       "      <td>0.000623</td>\n",
       "      <td>0.002082</td>\n",
       "      <td>0.002932</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-12-31</th>\n",
       "      <td>-0.018452</td>\n",
       "      <td>-0.024026</td>\n",
       "      <td>12 2024</td>\n",
       "      <td>2024 Q4</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>-0.018489</td>\n",
       "      <td>0.000623</td>\n",
       "      <td>0.002082</td>\n",
       "      <td>0.002932</td>\n",
       "      <td>-0.004398</td>\n",
       "      <td>-0.018489</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000623</td>\n",
       "      <td>0.002082</td>\n",
       "      <td>0.002932</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8361 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             MarketR  MarketR_e    month        Q     rfday        er  \\\n",
       "Day                                                                     \n",
       "1990-12-20  0.050067   0.752547  12 1990  1990 Q4  0.000227  0.049840   \n",
       "1990-12-21  0.039671  67.771445  12 1990  1990 Q4  0.000227  0.039444   \n",
       "1990-12-24  0.049380   0.049638  12 1990  1990 Q4  0.000227  0.049153   \n",
       "1990-12-25  0.049938   0.050022  12 1990  1990 Q4  0.000227  0.049711   \n",
       "1990-12-26  0.050151   0.050151  12 1990  1990 Q4  0.000227  0.049924   \n",
       "...              ...        ...      ...      ...       ...       ...   \n",
       "2024-12-25 -0.004361  -0.019226  12 2024  2024 Q4  0.000037 -0.004398   \n",
       "2024-12-26  0.002969   0.011729  12 2024  2024 Q4  0.000037  0.002932   \n",
       "2024-12-27  0.002119   0.008489  12 2024  2024 Q4  0.000037  0.002082   \n",
       "2024-12-30  0.000660  -0.008647  12 2024  2024 Q4  0.000037  0.000623   \n",
       "2024-12-31 -0.018452  -0.024026  12 2024  2024 Q4  0.000037 -0.018489   \n",
       "\n",
       "                 er1       er2       er3       er4    neg_er   neg_er1  \\\n",
       "Day                                                                      \n",
       "1990-12-20       NaN       NaN       NaN       NaN  0.000000       NaN   \n",
       "1990-12-21  0.049840       NaN       NaN       NaN  0.000000  0.000000   \n",
       "1990-12-24  0.039444  0.049840       NaN       NaN  0.000000  0.000000   \n",
       "1990-12-25  0.049153  0.039444  0.049840       NaN  0.000000  0.000000   \n",
       "1990-12-26  0.049711  0.049153  0.039444  0.049840  0.000000  0.000000   \n",
       "...              ...       ...       ...       ...       ...       ...   \n",
       "2024-12-25  0.012003 -0.009967 -0.000314 -0.001943 -0.004398  0.000000   \n",
       "2024-12-26 -0.004398  0.012003 -0.009967 -0.000314  0.000000 -0.004398   \n",
       "2024-12-27  0.002932 -0.004398  0.012003 -0.009967  0.000000  0.000000   \n",
       "2024-12-30  0.002082  0.002932 -0.004398  0.012003  0.000000  0.000000   \n",
       "2024-12-31  0.000623  0.002082  0.002932 -0.004398 -0.018489  0.000000   \n",
       "\n",
       "             neg_er2   neg_er3    pos_er   pos_er1   pos_er2   pos_er3  \n",
       "Day                                                                     \n",
       "1990-12-20       NaN       NaN  0.049840       NaN       NaN       NaN  \n",
       "1990-12-21       NaN       NaN  0.039444  0.049840       NaN       NaN  \n",
       "1990-12-24  0.000000       NaN  0.049153  0.039444  0.049840       NaN  \n",
       "1990-12-25  0.000000  0.000000  0.049711  0.049153  0.039444  0.049840  \n",
       "1990-12-26  0.000000  0.000000  0.049924  0.049711  0.049153  0.039444  \n",
       "...              ...       ...       ...       ...       ...       ...  \n",
       "2024-12-25 -0.009967 -0.000314  0.000000  0.012003  0.000000  0.000000  \n",
       "2024-12-26  0.000000 -0.009967  0.002932  0.000000  0.012003  0.000000  \n",
       "2024-12-27 -0.004398  0.000000  0.002082  0.002932  0.000000  0.012003  \n",
       "2024-12-30  0.000000 -0.004398  0.000623  0.002082  0.002932  0.000000  \n",
       "2024-12-31  0.000000  0.000000  0.000000  0.000623  0.002082  0.002932  \n",
       "\n",
       "[8361 rows x 18 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Market_ret_day = pd.read_excel('F:/Python2025/python/datasets/Marketret_day_stock2024.xlsx')\n",
    "Market_ret_day['Day'] = pd.to_datetime(Market_ret_day['Day'],format='%Y-%m-%d')\n",
    "Market_ret_day.set_index('Day',inplace=True)\n",
    "Market_ret_day.sort_index(inplace=True)\n",
    "Market_ret_day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  RV       RV1       RV2       RV3       var\n",
      "month                                                       \n",
      "1990-12-31  0.011709  0.030732  0.046608  0.058424  0.000395\n",
      "1991-01-31  0.000690  0.001566  0.002593  0.003461  0.000030\n",
      "1991-02-28  0.000418  0.000707  0.000853  0.000763  0.000024\n",
      "1991-03-31  0.001229  0.002925  0.004650  0.006369  0.000014\n",
      "1991-04-30  0.000855  0.001973  0.003364  0.004531  0.000010\n",
      "...              ...       ...       ...       ...       ...\n",
      "2024-08-31  0.000881  0.000823  0.000487  0.000487  0.000040\n",
      "2024-09-30  0.012481  0.023868  0.034838  0.041347  0.000593\n",
      "2024-10-31  0.011652  0.007954  0.006707  0.006707  0.000685\n",
      "2024-11-30  0.004019  0.004118  0.005938  0.003775  0.000201\n",
      "2024-12-31  0.001713  0.001517  0.001623  0.001167  0.000082\n",
      "\n",
      "[409 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "def calculate_monthly_variance(daily_data: pd.DataFrame) -> pd.DataFrame:\n",
    "    \"\"\"\n",
    "    基于日度超额收益，计算月度已实现方差（RV）及扩展指标（RV1/RV2/RV3），处理负数值并补充月度方差。\n",
    "    \n",
    "    参数:\n",
    "        daily_data: 日度数据，需包含 er、er1、er2、er3 列，索引为日期。\n",
    "    \n",
    "    返回:\n",
    "        月度方差指标 DataFrame，索引为月末日期（month），包含 RV、RV1、RV2、RV3、var 列。\n",
    "    \"\"\"\n",
    "    # 1. 定义月度聚合逻辑：一次计算所有指标，减少重复 resample\n",
    "    def monthly_agg_func(df: pd.DataFrame) -> pd.Series:\n",
    "        # 提取中间变量，避免重复计算（核心优化点）\n",
    "        er_sq_sum = (df['er'] ** 2).sum()  # 基础项：日er平方和（RV的核心）\n",
    "        er_er1_sum = (df['er'] * df['er1']).sum()\n",
    "        er_er2_sum = (df['er'] * df['er2']).sum()\n",
    "        er_er3_sum = (df['er'] * df['er3']).sum()\n",
    "        \n",
    "        # 计算各RV指标（按原定义）\n",
    "        rv = er_sq_sum\n",
    "        rv1 = rv + 2 * er_er1_sum\n",
    "        rv2 = rv1 + 2 * er_er2_sum\n",
    "        rv3 = rv2 + 2 * er_er3_sum\n",
    "        \n",
    "        # 计算月度方差（与原代码一致：默认ddof=1）\n",
    "        var = df['er'].var()\n",
    "        \n",
    "        return pd.Series({\n",
    "            'RV': rv,\n",
    "            'RV1': rv1,\n",
    "            'RV2': rv2,\n",
    "            'RV3': rv3,\n",
    "            'var': var\n",
    "        })\n",
    "    \n",
    "    # 2. 按月末频率聚合（ME = month end）\n",
    "    market_variance = daily_data.resample('ME').apply(monthly_agg_func)\n",
    "    \n",
    "    # 3. 处理负数值：按原逻辑（RV3<0→用RV2，RV2<0→用RV1，RV1<0→用RV）\n",
    "    # 用 np.select 简化条件判断，逻辑更清晰（核心优化点）\n",
    "    # 条件顺序：先判断更严格的RV3，再依次向下\n",
    "    conditions = [\n",
    "        market_variance['RV3'] >= 0,\n",
    "        market_variance['RV2'] >= 0,\n",
    "        market_variance['RV1'] >= 0\n",
    "    ]\n",
    "    choices = [\n",
    "        market_variance['RV3'],\n",
    "        market_variance['RV2'],\n",
    "        market_variance['RV1']\n",
    "    ]\n",
    "    # 若所有条件都不满足（即RV1<0），则用RV兜底\n",
    "    market_variance['RV3'] = np.select(conditions, choices, default=market_variance['RV'])\n",
    "    \n",
    "    # 同理处理RV2（基于已修正的RV3？不，原逻辑是独立判断RV2，再修正RV3）\n",
    "    # 还原原逻辑：先修正RV1，再RV2，最后RV3（顺序不能乱）\n",
    "    market_variance['RV1'] = np.where(market_variance['RV1'] >= 0, market_variance['RV1'], market_variance['RV'])\n",
    "    market_variance['RV2'] = np.where(market_variance['RV2'] >= 0, market_variance['RV2'], market_variance['RV1'])\n",
    "    market_variance['RV3'] = np.where(market_variance['RV3'] >= 0, market_variance['RV3'], market_variance['RV2'])\n",
    "    \n",
    "    # 4. 规范索引名称（与原代码一致）\n",
    "    market_variance.index.name = 'month'\n",
    "    \n",
    "    return market_variance\n",
    "\n",
    "# 调用函数计算（保持原结果不变）\n",
    "market_variance = calculate_monthly_variance(Market_ret_day)\n",
    "\n",
    "# 打印结果（与原代码输出一致）\n",
    "print(market_variance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MarketR</th>\n",
       "      <th>rfmonth</th>\n",
       "      <th>ret</th>\n",
       "      <th>cpi</th>\n",
       "      <th>RV</th>\n",
       "      <th>RV1</th>\n",
       "      <th>RV2</th>\n",
       "      <th>RV3</th>\n",
       "      <th>var</th>\n",
       "      <th>pd</th>\n",
       "      <th>pe</th>\n",
       "      <th>pb</th>\n",
       "      <th>marketret3</th>\n",
       "      <th>marketret6</th>\n",
       "      <th>marketret12</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1991-01-31</th>\n",
       "      <td>0.029998</td>\n",
       "      <td>0.006930</td>\n",
       "      <td>0.023068</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.000690</td>\n",
       "      <td>0.001566</td>\n",
       "      <td>0.002593</td>\n",
       "      <td>0.003461</td>\n",
       "      <td>0.000030</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.466562</td>\n",
       "      <td>2.080065</td>\n",
       "      <td>-0.084127</td>\n",
       "      <td>-0.305662</td>\n",
       "      <td>0.254049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-02-28</th>\n",
       "      <td>0.010203</td>\n",
       "      <td>0.006930</td>\n",
       "      <td>0.003273</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000418</td>\n",
       "      <td>0.000707</td>\n",
       "      <td>0.000853</td>\n",
       "      <td>0.000763</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.501042</td>\n",
       "      <td>1.949285</td>\n",
       "      <td>-0.183573</td>\n",
       "      <td>-0.384745</td>\n",
       "      <td>0.241492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-03-31</th>\n",
       "      <td>-0.099663</td>\n",
       "      <td>0.006930</td>\n",
       "      <td>-0.106593</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.001229</td>\n",
       "      <td>0.002925</td>\n",
       "      <td>0.004650</td>\n",
       "      <td>0.006369</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.112082</td>\n",
       "      <td>1.560324</td>\n",
       "      <td>-0.252928</td>\n",
       "      <td>-0.445049</td>\n",
       "      <td>0.288857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-04-30</th>\n",
       "      <td>-0.079652</td>\n",
       "      <td>0.006651</td>\n",
       "      <td>-0.086303</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.000855</td>\n",
       "      <td>0.001973</td>\n",
       "      <td>0.003364</td>\n",
       "      <td>0.004531</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.111121</td>\n",
       "      <td>1.970330</td>\n",
       "      <td>-0.234776</td>\n",
       "      <td>-0.394937</td>\n",
       "      <td>0.691749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-05-31</th>\n",
       "      <td>-0.074521</td>\n",
       "      <td>0.006092</td>\n",
       "      <td>-0.080613</td>\n",
       "      <td>3.6</td>\n",
       "      <td>0.000480</td>\n",
       "      <td>0.001262</td>\n",
       "      <td>0.002119</td>\n",
       "      <td>0.002949</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.182871</td>\n",
       "      <td>1.901101</td>\n",
       "      <td>-0.236294</td>\n",
       "      <td>0.181673</td>\n",
       "      <td>1.542701</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2024-08-31</th>\n",
       "      <td>-0.031468</td>\n",
       "      <td>0.001118</td>\n",
       "      <td>-0.032586</td>\n",
       "      <td>0.6</td>\n",
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       "      <td>0.097936</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2024-09-30</th>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2024-10-31</th>\n",
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       "      <td>0.254341</td>\n",
       "      <td>0.000228</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2024-11-30</th>\n",
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       "      <td>0.001118</td>\n",
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       "    <tr>\n",
       "      <th>2024-12-31</th>\n",
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       "      <td>NaN</td>\n",
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       "<p>408 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             MarketR   rfmonth       ret  cpi        RV       RV1       RV2  \\\n",
       "month                                                                         \n",
       "1991-01-31  0.029998  0.006930  0.023068  2.2  0.000690  0.001566  0.002593   \n",
       "1991-02-28  0.010203  0.006930  0.003273  1.0  0.000418  0.000707  0.000853   \n",
       "1991-03-31 -0.099663  0.006930 -0.106593  1.6  0.001229  0.002925  0.004650   \n",
       "1991-04-30 -0.079652  0.006651 -0.086303  1.3  0.000855  0.001973  0.003364   \n",
       "1991-05-31 -0.074521  0.006092 -0.080613  3.6  0.000480  0.001262  0.002119   \n",
       "...              ...       ...       ...  ...       ...       ...       ...   \n",
       "2024-08-31 -0.031468  0.001118 -0.032586  0.6  0.000881  0.000823  0.000487   \n",
       "2024-09-30  0.195992  0.001118  0.194874  0.4  0.012481  0.023868  0.034838   \n",
       "2024-10-31 -0.003409  0.001118 -0.004527  0.3  0.011652  0.007954  0.006707   \n",
       "2024-11-30  0.009724  0.001118  0.008606  0.2  0.004019  0.004118  0.005938   \n",
       "2024-12-31 -0.002679  0.001118 -0.003797  0.1  0.001713  0.001517  0.001623   \n",
       "\n",
       "                 RV3       var         pd        pe        pb  marketret3  \\\n",
       "month                                                                       \n",
       "1991-01-31  0.003461  0.000030        NaN  4.466562  2.080065   -0.084127   \n",
       "1991-02-28  0.000763  0.000024        NaN  4.501042  1.949285   -0.183573   \n",
       "1991-03-31  0.006369  0.000014        NaN  4.112082  1.560324   -0.252928   \n",
       "1991-04-30  0.004531  0.000010        NaN  4.111121  1.970330   -0.234776   \n",
       "1991-05-31  0.002949  0.000005        NaN  4.182871  1.901101   -0.236294   \n",
       "...              ...       ...        ...       ...       ...         ...   \n",
       "2024-08-31  0.000487  0.000040  10.728048  2.502897  0.097936    0.151050   \n",
       "2024-09-30  0.041347  0.000593  10.889979  2.675435  0.275764    0.200147   \n",
       "2024-10-31  0.006707  0.000685  10.862945  2.635010  0.254341    0.000228   \n",
       "2024-11-30  0.003775  0.000201  10.858076  2.643930  0.263924         NaN   \n",
       "2024-12-31  0.001167  0.000082  10.842070  2.652927  0.261665         NaN   \n",
       "\n",
       "            marketret6  marketret12  \n",
       "month                                \n",
       "1991-01-31   -0.305662     0.254049  \n",
       "1991-02-28   -0.384745     0.241492  \n",
       "1991-03-31   -0.445049     0.288857  \n",
       "1991-04-30   -0.394937     0.691749  \n",
       "1991-05-31    0.181673     1.542701  \n",
       "...                ...          ...  \n",
       "2024-08-31         NaN          NaN  \n",
       "2024-09-30         NaN          NaN  \n",
       "2024-10-31         NaN          NaN  \n",
       "2024-11-30         NaN          NaN  \n",
       "2024-12-31         NaN          NaN  \n",
       "\n",
       "[408 rows x 15 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_data = pd.merge(Market_ret,inflation,on = 'month')\n",
    "reg_data = pd.merge(reg_data,market_variance,on = 'month')\n",
    "reg_data = pd.merge(reg_data,price_dividend,on = 'month')\n",
    "reg_data = pd.merge(reg_data,price_earning,on = 'month')\n",
    "reg_data = pd.merge(reg_data,price_bookvalue,on = 'month')\n",
    "reg_data = reg_data[['MarketR','rfmonth','ret','cpi','RV','RV1','RV2','RV3','var','pd','pe','pb','marketret3','marketret6','marketret12']]\n",
    "# Output reg_data to reg_data.csv\n",
    "reg_data.to_csv('F:/Python2025/python/datasets/reg_data.csv')\n",
    "# Output reg_data to reg_data.xlsx\n",
    "reg_data.to_excel('F:/Python2025/python/datasets/reg_data.xlsx')\n",
    "\n",
    "reg_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\闫佳欣\\AppData\\Local\\Temp\\ipykernel_5356\\2752508842.py:2: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  dates = pd.date_range(start='2000-01-01', end='2024-12-31', freq='M')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Variance')"
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     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'China Stock Market: RV vs RV3 vs Variance')"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
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    {
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      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
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   "source": [
    "np.random.seed(42)\n",
    "dates = pd.date_range(start='2000-01-01', end='2024-12-31', freq='M')\n",
    "n = len(dates)\n",
    "reg_data = pd.DataFrame({\n",
    "    'RV': np.cumsum(np.random.randn(n) * 0.001) + 0.02,\n",
    "    'RV3': np.cumsum(np.random.randn(n) * 0.0015) + 0.025,\n",
    "    'var': np.cumsum(np.random.randn(n) * 0.0008) + 0.018\n",
    "}, index=dates)\n",
    "\n",
    "# 1. 使用 .loc 进行切片，更明确且高效\n",
    "reg_data_plot = reg_data.loc['2000-01':'2024-12']\n",
    "\n",
    "# 2. 创建图形和轴\n",
    "fig, ax1 = plt.subplots(figsize=(12, 6))\n",
    "\n",
    "# 3. 定义绘图函数和系列列表，减少重复代码\n",
    "def plot_series(series, color, label):\n",
    "    ax1.plot(series.index, series.values, color=color, marker='o', \n",
    "             linewidth=0.8, markersize=4, linestyle='-', label=label)\n",
    "\n",
    "series_to_plot = [\n",
    "    ('RV3', 'blue', 'Realized Variance (RV3)'),\n",
    "    ('var', 'green', 'Variance (var)'),\n",
    "    ('RV', 'red', 'Realized Variance (RV)')\n",
    "]\n",
    "\n",
    "for col, color, label in series_to_plot:\n",
    "    plot_series(reg_data_plot[col], color, label)\n",
    "\n",
    "# 4. 设置标签、标题和图例\n",
    "ax1.set_ylabel('Variance')\n",
    "ax1.set_title(\"China Stock Market: RV vs RV3 vs Variance\", fontsize=16)\n",
    "ax1.legend(loc='upper left')\n",
    "\n",
    "# 5. 调整布局并显示\n",
    "plt.tight_layout()\n",
    "plt.show()"
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   "cell_type": "code",
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     "name": "stderr",
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     "text": [
      "C:\\Users\\闫佳欣\\AppData\\Local\\Temp\\ipykernel_5356\\921731182.py:2: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  dates = pd.date_range(start='2000-01-01', end='2024-12-31', freq='M')\n"
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      ],
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "dates = pd.date_range(start='2000-01-01', end='2024-12-31', freq='M')\n",
    "n = len(dates)\n",
    "reg_data = pd.DataFrame({\n",
    "    'ret': np.random.randn(n) * 0.05,\n",
    "    'RV3': np.abs(np.random.randn(n) * 0.005) + 0.01\n",
    "}, index=dates)\n",
    "\n",
    "# 1. 数据准备与切片\n",
    "reg_data_plot = reg_data.loc['2000-01':'2024-12']\n",
    "\n",
    "# 2. 创建图形和轴对象\n",
    "fig, ax1 = plt.subplots(figsize=(12, 6))\n",
    "ax2 = ax1.twinx() # 创建共享x轴的第二个y轴\n",
    "\n",
    "# 3. 定义通用绘图参数，避免重复\n",
    "plot_kwargs = {\n",
    "    'marker': 'o',\n",
    "    'linewidth': 0.8,\n",
    "    'markersize': 4,\n",
    "    'linestyle': '-'\n",
    "}\n",
    "\n",
    "# 4. 绘制两条线\n",
    "ax1.plot(reg_data_plot.index, reg_data_plot['ret'], color='red', label='China Stock Market Return', **plot_kwargs)\n",
    "ax2.plot(reg_data_plot.index, reg_data_plot['RV3'].shift(1), color='blue', label='Realized Variance (RV3, lagged)', **plot_kwargs)\n",
    "\n",
    "# 5. 合并图例\n",
    "lines1, labels1 = ax1.get_legend_handles_labels()\n",
    "lines2, labels2 = ax2.get_legend_handles_labels()\n",
    "ax1.legend(lines1 + lines2, labels1 + labels2, loc='upper right', frameon=True, fancybox=True, shadow=True)\n",
    "\n",
    "# 6. 设置标题和轴标签\n",
    "ax1.set_title('China Stock Market Return and Realized Variance (RV3)', fontsize=16)\n",
    "ax1.set_ylabel('China Stock Market Return', color='red', fontsize=12)\n",
    "ax2.set_ylabel('Realized Variance', color='blue', fontsize=12)\n",
    "ax1.set_xlabel('Year', fontsize=12)\n",
    "\n",
    "# 7. 格式化x轴日期\n",
    "ax1.xaxis.set_major_locator(mdates.YearLocator())\n",
    "ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y'))\n",
    "plt.setp(ax1.xaxis.get_majorticklabels(), rotation=45, ha='right') # 旋转45度并右对齐，更美观\n",
    "\n",
    "# 8. 微调布局并显示\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "描述性统计"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    408.00000\n",
       "mean       0.00876\n",
       "std        0.01858\n",
       "min        0.00027\n",
       "25%        0.00189\n",
       "50%        0.00357\n",
       "75%        0.00882\n",
       "max        0.25211\n",
       "Name: RV, dtype: float64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_data['RV'].describe().round(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.734617011548641"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "82.80411751131915"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_data['RV'].skew()\n",
    "reg_data['RV'].kurt()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "原始序列的ADF检验结果:\n",
      "ADF Statistic: -3.4302\n",
      "p-value: 0.0100\n",
      "Critical Values:\n",
      "   1%: -3.4527\n",
      "   5%: -2.8714\n",
      "   10%: -2.5720\n",
      "结论: p-value小于0.05，拒绝原假设，序列是平稳的。\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.tsa.stattools import adfuller as ADF\n",
    "\n",
    "# 对月收益率数据进行ADF检验\n",
    "adf_result = ADF(reg_data[reg_data.index >= '2000-01-31']['RV'])\n",
    "\n",
    "print('原始序列的ADF检验结果:')\n",
    "print(f'ADF Statistic: {adf_result[0]:.4f}')\n",
    "print(f'p-value: {adf_result[1]:.4f}')\n",
    "print('Critical Values:')\n",
    "for key, value in adf_result[4].items():\n",
    "    print(f'   {key}: {value:.4f}')\n",
    "\n",
    "if adf_result[1] <= 0.05:\n",
    "    print('结论: p-value小于0.05，拒绝原假设，序列是平稳的。')\n",
    "else:\n",
    "    print('结论: p-value大于0.05，未能拒绝原假设，序列是非平稳的。')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                    ret   R-squared:                       0.001\n",
      "Model:                            OLS   Adj. R-squared:                 -0.002\n",
      "Method:                 Least Squares   F-statistic:                    0.2224\n",
      "Date:                Sat, 15 Nov 2025   Prob (F-statistic):              0.637\n",
      "Time:                        00:22:29   Log-Likelihood:                 390.73\n",
      "No. Observations:                 360   AIC:                            -777.5\n",
      "Df Residuals:                     358   BIC:                            -769.7\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:                  HAC                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      0.0099      0.005      1.923      0.054      -0.000       0.020\n",
      "lRV           -0.2266      0.481     -0.472      0.637      -1.169       0.715\n",
      "==============================================================================\n",
      "Omnibus:                       32.778   Durbin-Watson:                   1.803\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               78.386\n",
      "Skew:                           0.448   Prob(JB):                     9.52e-18\n",
      "Kurtosis:                       5.103   Cond. No.                         97.3\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors are heteroscedasticity and autocorrelation robust (HAC) using 6 lags and without small sample correction\n"
     ]
    }
   ],
   "source": [
    "reg_data['lRV'] = reg_data['RV'].shift(1)\n",
    "model_cpi = smf.ols('ret ~ lRV',\n",
    "                 data=reg_data['1995-01':'2024-12']).fit(\n",
    "                     cov_type='HAC', cov_kwds={'maxlags': 6})\n",
    "print(model_cpi.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                    ret   R-squared:                       0.034\n",
      "Model:                            OLS   Adj. R-squared:                  0.031\n",
      "Method:                 Least Squares   F-statistic:                     7.293\n",
      "Date:                Sat, 15 Nov 2025   Prob (F-statistic):            0.00732\n",
      "Time:                        00:22:44   Log-Likelihood:                 354.12\n",
      "No. Observations:                 300   AIC:                            -704.2\n",
      "Df Residuals:                     298   BIC:                            -696.8\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:                  HAC                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      0.0213      0.007      2.874      0.004       0.007       0.036\n",
      "lcpi          -0.0073      0.003     -2.701      0.007      -0.013      -0.002\n",
      "==============================================================================\n",
      "Omnibus:                       14.234   Durbin-Watson:                   1.755\n",
      "Prob(Omnibus):                  0.001   Jarque-Bera (JB):               33.078\n",
      "Skew:                           0.111   Prob(JB):                     6.57e-08\n",
      "Kurtosis:                       4.612   Cond. No.                         4.32\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors are heteroscedasticity and autocorrelation robust (HAC) using 6 lags and without small sample correction\n"
     ]
    }
   ],
   "source": [
    "reg_data['lcpi'] = reg_data['cpi'].shift(2)\n",
    "model_cpi = smf.ols('ret ~ lcpi',\n",
    "                 data=reg_data['2000-01':'2024-12']).fit(\n",
    "                     cov_type='HAC', cov_kwds={'maxlags': 6})\n",
    "print(model_cpi.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                    ret   R-squared:                       0.034\n",
      "Model:                            OLS   Adj. R-squared:                  0.028\n",
      "Method:                 Least Squares   F-statistic:                     3.773\n",
      "Date:                Sat, 15 Nov 2025   Prob (F-statistic):             0.0241\n",
      "Time:                        00:22:58   Log-Likelihood:                 354.12\n",
      "No. Observations:                 300   AIC:                            -702.2\n",
      "Df Residuals:                     297   BIC:                            -691.1\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:                  HAC                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      0.0212      0.008      2.611      0.009       0.005       0.037\n",
      "lRV            0.0317      1.186      0.027      0.979      -2.293       2.356\n",
      "lcpi          -0.0073      0.003     -2.718      0.007      -0.013      -0.002\n",
      "==============================================================================\n",
      "Omnibus:                       14.234   Durbin-Watson:                   1.754\n",
      "Prob(Omnibus):                  0.001   Jarque-Bera (JB):               33.202\n",
      "Skew:                           0.107   Prob(JB):                     6.17e-08\n",
      "Kurtosis:                       4.616   Cond. No.                         465.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors are heteroscedasticity and autocorrelation robust (HAC) using 6 lags and without small sample correction\n"
     ]
    }
   ],
   "source": [
    "model_twovariables = smf.ols('ret ~ lRV + lcpi',\n",
    "                 data=reg_data['2000-01':'2024-12']).fit(\n",
    "                     cov_type='HAC', cov_kwds={'maxlags': 6})\n",
    "print(model_twovariables.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                    ret   R-squared:                       0.037\n",
      "Model:                            OLS   Adj. R-squared:                  0.027\n",
      "Method:                 Least Squares   F-statistic:                     2.581\n",
      "Date:                Sat, 15 Nov 2025   Prob (F-statistic):             0.0537\n",
      "Time:                        00:23:12   Log-Likelihood:                 354.45\n",
      "No. Observations:                 300   AIC:                            -700.9\n",
      "Df Residuals:                     296   BIC:                            -686.1\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:                  HAC                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      0.0826      0.088      0.937      0.349      -0.090       0.255\n",
      "lRV            0.1355      1.216      0.111      0.911      -2.249       2.520\n",
      "lpd           -0.0053      0.008     -0.696      0.487      -0.020       0.010\n",
      "lcpi          -0.0075      0.003     -2.749      0.006      -0.013      -0.002\n",
      "==============================================================================\n",
      "Omnibus:                       15.099   Durbin-Watson:                   1.746\n",
      "Prob(Omnibus):                  0.001   Jarque-Bera (JB):               35.725\n",
      "Skew:                           0.134   Prob(JB):                     1.75e-08\n",
      "Kurtosis:                       4.669   Cond. No.                     1.93e+03\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors are heteroscedasticity and autocorrelation robust (HAC) using 6 lags and without small sample correction\n",
      "[2] The condition number is large, 1.93e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "reg_data['lpd'] = reg_data['pd'].shift(1)\n",
    "model3 = smf.ols('ret ~ lRV + lpd + lcpi',\n",
    "                 data=reg_data['2000-01':'2024-12']).fit(\n",
    "                     cov_type='HAC', cov_kwds={'maxlags': 6})\n",
    "print(model3.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "季度结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RV</th>\n",
       "      <th>RV1</th>\n",
       "      <th>RV2</th>\n",
       "      <th>RV3</th>\n",
       "      <th>var</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-12-31</th>\n",
       "      <td>0.011709</td>\n",
       "      <td>0.030732</td>\n",
       "      <td>0.046608</td>\n",
       "      <td>0.058424</td>\n",
       "      <td>0.000395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-03-31</th>\n",
       "      <td>0.002337</td>\n",
       "      <td>0.005198</td>\n",
       "      <td>0.008096</td>\n",
       "      <td>0.010593</td>\n",
       "      <td>0.000014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-06-30</th>\n",
       "      <td>0.006177</td>\n",
       "      <td>0.008025</td>\n",
       "      <td>0.008207</td>\n",
       "      <td>0.010588</td>\n",
       "      <td>0.000196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-09-30</th>\n",
       "      <td>0.119336</td>\n",
       "      <td>0.114575</td>\n",
       "      <td>0.105919</td>\n",
       "      <td>0.105381</td>\n",
       "      <td>0.000262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-12-31</th>\n",
       "      <td>0.155513</td>\n",
       "      <td>0.176020</td>\n",
       "      <td>0.199149</td>\n",
       "      <td>0.234951</td>\n",
       "      <td>0.000244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-31</th>\n",
       "      <td>0.003146</td>\n",
       "      <td>0.004187</td>\n",
       "      <td>0.004378</td>\n",
       "      <td>0.004791</td>\n",
       "      <td>0.000061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-31</th>\n",
       "      <td>0.009678</td>\n",
       "      <td>0.012825</td>\n",
       "      <td>0.014335</td>\n",
       "      <td>0.013999</td>\n",
       "      <td>0.000058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-06-30</th>\n",
       "      <td>0.004243</td>\n",
       "      <td>0.002390</td>\n",
       "      <td>0.003917</td>\n",
       "      <td>0.003171</td>\n",
       "      <td>0.000047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-30</th>\n",
       "      <td>0.015241</td>\n",
       "      <td>0.026383</td>\n",
       "      <td>0.037398</td>\n",
       "      <td>0.042365</td>\n",
       "      <td>0.000593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-12-31</th>\n",
       "      <td>0.017384</td>\n",
       "      <td>0.013589</td>\n",
       "      <td>0.014268</td>\n",
       "      <td>0.003617</td>\n",
       "      <td>0.000082</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>137 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  RV       RV1       RV2       RV3       var\n",
       "Q                                                           \n",
       "1990-12-31  0.011709  0.030732  0.046608  0.058424  0.000395\n",
       "1991-03-31  0.002337  0.005198  0.008096  0.010593  0.000014\n",
       "1991-06-30  0.006177  0.008025  0.008207  0.010588  0.000196\n",
       "1991-09-30  0.119336  0.114575  0.105919  0.105381  0.000262\n",
       "1991-12-31  0.155513  0.176020  0.199149  0.234951  0.000244\n",
       "...              ...       ...       ...       ...       ...\n",
       "2023-12-31  0.003146  0.004187  0.004378  0.004791  0.000061\n",
       "2024-03-31  0.009678  0.012825  0.014335  0.013999  0.000058\n",
       "2024-06-30  0.004243  0.002390  0.003917  0.003171  0.000047\n",
       "2024-09-30  0.015241  0.026383  0.037398  0.042365  0.000593\n",
       "2024-12-31  0.017384  0.013589  0.014268  0.003617  0.000082\n",
       "\n",
       "[137 rows x 5 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate monthly realized variance (RV) by summing squared excess returns\n",
    "Market_variance_Q = Market_ret_day.resample('QE').apply(lambda df: pd.Series({\n",
    "    'RV': (df['er']**2).sum(),\n",
    "    'RV1': (df['er']**2).sum() + 2*(df['er']*df['er1']).sum(),\n",
    "    'RV2': (df['er']**2).sum() + 2*(df['er']*df['er1']).sum() + 2*(df['er']*df['er2']).sum(),\n",
    "    'RV3': (df['er']**2).sum() + 2*(df['er']*df['er1']).sum() + 2*(df['er']*df['er2']).sum() + 2*(df['er']*df['er3']).sum()\n",
    "}))\n",
    "\n",
    "Market_variance_Q['var'] = Market_ret_day.resample('ME')['er'].var()\n",
    "Market_variance_Q.index.name = 'month'\n",
    "\n",
    "# if RV3 <0, set to RV2 if RV2 <0, set to RV1 if RV1 <0, set to RV\n",
    "Market_variance_Q['RV1'] = Market_variance_Q['RV1'].where(Market_variance_Q['RV1']>=0, Market_variance_Q['RV'])\n",
    "Market_variance_Q['RV2'] = Market_variance_Q['RV2'].where(Market_variance_Q['RV2']>=0, Market_variance_Q['RV1'])\n",
    "Market_variance_Q['RV3'] = Market_variance_Q['RV3'].where(Market_variance_Q['RV3']>=0, Market_variance_Q['RV2'])\n",
    "Market_variance_Q.index.name = 'Q'\n",
    "Market_variance_Q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MarketR</th>\n",
       "      <th>rfqtr</th>\n",
       "      <th>cpi</th>\n",
       "      <th>pd</th>\n",
       "      <th>pe</th>\n",
       "      <th>pb</th>\n",
       "      <th>ret</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1995-03-31</th>\n",
       "      <td>-0.014687</td>\n",
       "      <td>0.026386</td>\n",
       "      <td>6.780000e+01</td>\n",
       "      <td>11.798194</td>\n",
       "      <td>2.844483</td>\n",
       "      <td>0.866200</td>\n",
       "      <td>-0.041073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-06-30</th>\n",
       "      <td>-0.037718</td>\n",
       "      <td>0.026386</td>\n",
       "      <td>5.920000e+01</td>\n",
       "      <td>11.432152</td>\n",
       "      <td>2.655143</td>\n",
       "      <td>0.667672</td>\n",
       "      <td>-0.064104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-09-30</th>\n",
       "      <td>0.115336</td>\n",
       "      <td>0.026386</td>\n",
       "      <td>4.440000e+01</td>\n",
       "      <td>11.317123</td>\n",
       "      <td>2.777629</td>\n",
       "      <td>0.732792</td>\n",
       "      <td>0.088951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-12-31</th>\n",
       "      <td>-0.181765</td>\n",
       "      <td>0.026386</td>\n",
       "      <td>3.340000e+01</td>\n",
       "      <td>11.207058</td>\n",
       "      <td>2.572886</td>\n",
       "      <td>0.556556</td>\n",
       "      <td>-0.208151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-03-31</th>\n",
       "      <td>0.003789</td>\n",
       "      <td>0.026386</td>\n",
       "      <td>2.810000e+01</td>\n",
       "      <td>11.253253</td>\n",
       "      <td>2.565481</td>\n",
       "      <td>0.560128</td>\n",
       "      <td>-0.022597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-31</th>\n",
       "      <td>-0.044089</td>\n",
       "      <td>0.003854</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>10.817151</td>\n",
       "      <td>2.527098</td>\n",
       "      <td>0.194641</td>\n",
       "      <td>-0.047943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-31</th>\n",
       "      <td>0.010521</td>\n",
       "      <td>0.003699</td>\n",
       "      <td>-8.326673e-17</td>\n",
       "      <td>10.831573</td>\n",
       "      <td>2.559242</td>\n",
       "      <td>0.195646</td>\n",
       "      <td>0.006822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-06-30</th>\n",
       "      <td>-0.030634</td>\n",
       "      <td>0.003604</td>\n",
       "      <td>8.000000e-01</td>\n",
       "      <td>10.770461</td>\n",
       "      <td>2.557868</td>\n",
       "      <td>0.136221</td>\n",
       "      <td>-0.034238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-30</th>\n",
       "      <td>0.159005</td>\n",
       "      <td>0.003424</td>\n",
       "      <td>1.500000e+00</td>\n",
       "      <td>10.889979</td>\n",
       "      <td>2.675435</td>\n",
       "      <td>0.275764</td>\n",
       "      <td>0.155581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-12-31</th>\n",
       "      <td>0.003586</td>\n",
       "      <td>0.003358</td>\n",
       "      <td>6.000000e-01</td>\n",
       "      <td>10.842070</td>\n",
       "      <td>2.652927</td>\n",
       "      <td>0.261665</td>\n",
       "      <td>0.000228</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>120 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             MarketR     rfqtr           cpi         pd        pe        pb  \\\n",
       "Q                                                                             \n",
       "1995-03-31 -0.014687  0.026386  6.780000e+01  11.798194  2.844483  0.866200   \n",
       "1995-06-30 -0.037718  0.026386  5.920000e+01  11.432152  2.655143  0.667672   \n",
       "1995-09-30  0.115336  0.026386  4.440000e+01  11.317123  2.777629  0.732792   \n",
       "1995-12-31 -0.181765  0.026386  3.340000e+01  11.207058  2.572886  0.556556   \n",
       "1996-03-31  0.003789  0.026386  2.810000e+01  11.253253  2.565481  0.560128   \n",
       "...              ...       ...           ...        ...       ...       ...   \n",
       "2023-12-31 -0.044089  0.003854 -1.000000e+00  10.817151  2.527098  0.194641   \n",
       "2024-03-31  0.010521  0.003699 -8.326673e-17  10.831573  2.559242  0.195646   \n",
       "2024-06-30 -0.030634  0.003604  8.000000e-01  10.770461  2.557868  0.136221   \n",
       "2024-09-30  0.159005  0.003424  1.500000e+00  10.889979  2.675435  0.275764   \n",
       "2024-12-31  0.003586  0.003358  6.000000e-01  10.842070  2.652927  0.261665   \n",
       "\n",
       "                 ret  \n",
       "Q                     \n",
       "1995-03-31 -0.041073  \n",
       "1995-06-30 -0.064104  \n",
       "1995-09-30  0.088951  \n",
       "1995-12-31 -0.208151  \n",
       "1996-03-31 -0.022597  \n",
       "...              ...  \n",
       "2023-12-31 -0.047943  \n",
       "2024-03-31  0.006822  \n",
       "2024-06-30 -0.034238  \n",
       "2024-09-30  0.155581  \n",
       "2024-12-31  0.000228  \n",
       "\n",
       "[120 rows x 7 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Market_ret_day复合成季收益率数据\n",
    "Q_marketret = reg_data['1995-01':'2024-12'].resample('QE').apply(\n",
    "    lambda df: pd.Series({\n",
    "        'MarketR': np.prod(1 + df['MarketR']) - 1,\n",
    "        'rfqtr': np.prod(1 + df['rfmonth']) - 1,\n",
    "        'cpi': sum(df['cpi']),\n",
    "        'pd': df['pd'].iloc[-1],\n",
    "        'pe': df['pe'].iloc[-1],\n",
    "        'pb': df['pb'].iloc[-1]\n",
    "    })\n",
    ")\n",
    "Q_marketret['ret'] = Q_marketret['MarketR'] - Q_marketret['rfqtr']\n",
    "Q_marketret.index.name = 'Q'\n",
    "Q_marketret"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MarketR</th>\n",
       "      <th>rfqtr</th>\n",
       "      <th>cpi</th>\n",
       "      <th>pd</th>\n",
       "      <th>pe</th>\n",
       "      <th>pb</th>\n",
       "      <th>ret</th>\n",
       "      <th>RV</th>\n",
       "      <th>RV1</th>\n",
       "      <th>RV2</th>\n",
       "      <th>RV3</th>\n",
       "      <th>var</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1995-03-31</th>\n",
       "      <td>-0.014687</td>\n",
       "      <td>0.026386</td>\n",
       "      <td>6.780000e+01</td>\n",
       "      <td>11.798194</td>\n",
       "      <td>2.844483</td>\n",
       "      <td>0.866200</td>\n",
       "      <td>-0.041073</td>\n",
       "      <td>0.035401</td>\n",
       "      <td>0.036770</td>\n",
       "      <td>0.030744</td>\n",
       "      <td>0.026817</td>\n",
       "      <td>0.000463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-06-30</th>\n",
       "      <td>-0.037718</td>\n",
       "      <td>0.026386</td>\n",
       "      <td>5.920000e+01</td>\n",
       "      <td>11.432152</td>\n",
       "      <td>2.655143</td>\n",
       "      <td>0.667672</td>\n",
       "      <td>-0.064104</td>\n",
       "      <td>0.165481</td>\n",
       "      <td>0.233823</td>\n",
       "      <td>0.240724</td>\n",
       "      <td>0.114255</td>\n",
       "      <td>0.000387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-09-30</th>\n",
       "      <td>0.115336</td>\n",
       "      <td>0.026386</td>\n",
       "      <td>4.440000e+01</td>\n",
       "      <td>11.317123</td>\n",
       "      <td>2.777629</td>\n",
       "      <td>0.732792</td>\n",
       "      <td>0.088951</td>\n",
       "      <td>0.018665</td>\n",
       "      <td>0.016207</td>\n",
       "      <td>0.015538</td>\n",
       "      <td>0.018267</td>\n",
       "      <td>0.000157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-12-31</th>\n",
       "      <td>-0.181765</td>\n",
       "      <td>0.026386</td>\n",
       "      <td>3.340000e+01</td>\n",
       "      <td>11.207058</td>\n",
       "      <td>2.572886</td>\n",
       "      <td>0.556556</td>\n",
       "      <td>-0.208151</td>\n",
       "      <td>0.013791</td>\n",
       "      <td>0.008836</td>\n",
       "      <td>0.011401</td>\n",
       "      <td>0.019522</td>\n",
       "      <td>0.000153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-03-31</th>\n",
       "      <td>0.003789</td>\n",
       "      <td>0.026386</td>\n",
       "      <td>2.810000e+01</td>\n",
       "      <td>11.253253</td>\n",
       "      <td>2.565481</td>\n",
       "      <td>0.560128</td>\n",
       "      <td>-0.022597</td>\n",
       "      <td>0.019166</td>\n",
       "      <td>0.018851</td>\n",
       "      <td>0.009342</td>\n",
       "      <td>0.014716</td>\n",
       "      <td>0.000536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-31</th>\n",
       "      <td>-0.044089</td>\n",
       "      <td>0.003854</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>10.817151</td>\n",
       "      <td>2.527098</td>\n",
       "      <td>0.194641</td>\n",
       "      <td>-0.047943</td>\n",
       "      <td>0.003146</td>\n",
       "      <td>0.004187</td>\n",
       "      <td>0.004378</td>\n",
       "      <td>0.004791</td>\n",
       "      <td>0.000061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-31</th>\n",
       "      <td>0.010521</td>\n",
       "      <td>0.003699</td>\n",
       "      <td>-8.326673e-17</td>\n",
       "      <td>10.831573</td>\n",
       "      <td>2.559242</td>\n",
       "      <td>0.195646</td>\n",
       "      <td>0.006822</td>\n",
       "      <td>0.009678</td>\n",
       "      <td>0.012825</td>\n",
       "      <td>0.014335</td>\n",
       "      <td>0.013999</td>\n",
       "      <td>0.000058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-06-30</th>\n",
       "      <td>-0.030634</td>\n",
       "      <td>0.003604</td>\n",
       "      <td>8.000000e-01</td>\n",
       "      <td>10.770461</td>\n",
       "      <td>2.557868</td>\n",
       "      <td>0.136221</td>\n",
       "      <td>-0.034238</td>\n",
       "      <td>0.004243</td>\n",
       "      <td>0.002390</td>\n",
       "      <td>0.003917</td>\n",
       "      <td>0.003171</td>\n",
       "      <td>0.000047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-30</th>\n",
       "      <td>0.159005</td>\n",
       "      <td>0.003424</td>\n",
       "      <td>1.500000e+00</td>\n",
       "      <td>10.889979</td>\n",
       "      <td>2.675435</td>\n",
       "      <td>0.275764</td>\n",
       "      <td>0.155581</td>\n",
       "      <td>0.015241</td>\n",
       "      <td>0.026383</td>\n",
       "      <td>0.037398</td>\n",
       "      <td>0.042365</td>\n",
       "      <td>0.000593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-12-31</th>\n",
       "      <td>0.003586</td>\n",
       "      <td>0.003358</td>\n",
       "      <td>6.000000e-01</td>\n",
       "      <td>10.842070</td>\n",
       "      <td>2.652927</td>\n",
       "      <td>0.261665</td>\n",
       "      <td>0.000228</td>\n",
       "      <td>0.017384</td>\n",
       "      <td>0.013589</td>\n",
       "      <td>0.014268</td>\n",
       "      <td>0.003617</td>\n",
       "      <td>0.000082</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>120 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             MarketR     rfqtr           cpi         pd        pe        pb  \\\n",
       "Q                                                                             \n",
       "1995-03-31 -0.014687  0.026386  6.780000e+01  11.798194  2.844483  0.866200   \n",
       "1995-06-30 -0.037718  0.026386  5.920000e+01  11.432152  2.655143  0.667672   \n",
       "1995-09-30  0.115336  0.026386  4.440000e+01  11.317123  2.777629  0.732792   \n",
       "1995-12-31 -0.181765  0.026386  3.340000e+01  11.207058  2.572886  0.556556   \n",
       "1996-03-31  0.003789  0.026386  2.810000e+01  11.253253  2.565481  0.560128   \n",
       "...              ...       ...           ...        ...       ...       ...   \n",
       "2023-12-31 -0.044089  0.003854 -1.000000e+00  10.817151  2.527098  0.194641   \n",
       "2024-03-31  0.010521  0.003699 -8.326673e-17  10.831573  2.559242  0.195646   \n",
       "2024-06-30 -0.030634  0.003604  8.000000e-01  10.770461  2.557868  0.136221   \n",
       "2024-09-30  0.159005  0.003424  1.500000e+00  10.889979  2.675435  0.275764   \n",
       "2024-12-31  0.003586  0.003358  6.000000e-01  10.842070  2.652927  0.261665   \n",
       "\n",
       "                 ret        RV       RV1       RV2       RV3       var  \n",
       "Q                                                                       \n",
       "1995-03-31 -0.041073  0.035401  0.036770  0.030744  0.026817  0.000463  \n",
       "1995-06-30 -0.064104  0.165481  0.233823  0.240724  0.114255  0.000387  \n",
       "1995-09-30  0.088951  0.018665  0.016207  0.015538  0.018267  0.000157  \n",
       "1995-12-31 -0.208151  0.013791  0.008836  0.011401  0.019522  0.000153  \n",
       "1996-03-31 -0.022597  0.019166  0.018851  0.009342  0.014716  0.000536  \n",
       "...              ...       ...       ...       ...       ...       ...  \n",
       "2023-12-31 -0.047943  0.003146  0.004187  0.004378  0.004791  0.000061  \n",
       "2024-03-31  0.006822  0.009678  0.012825  0.014335  0.013999  0.000058  \n",
       "2024-06-30 -0.034238  0.004243  0.002390  0.003917  0.003171  0.000047  \n",
       "2024-09-30  0.155581  0.015241  0.026383  0.037398  0.042365  0.000593  \n",
       "2024-12-31  0.000228  0.017384  0.013589  0.014268  0.003617  0.000082  \n",
       "\n",
       "[120 rows x 12 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Qreg_data = pd.merge(Q_marketret,Market_variance_Q,on = 'Q')\n",
    "Qreg_data"
   ]
  }
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